Design and validation of a robust active trailer steering system for multi-trailer articulated heavy vehicles
Bibliographic record
Abstract
Multi-trailer articulated heavy vehicles (MTAHVs) are increasingly used around the world due to their economic and environmental benefits. However, MTAHVs exhibit poor maneuverability and low lateral stability, which may lead to fatal traffic accidents. Given the safety risks, it is necessary to solve the steering and stability problems of MTAHVs before they are safely mass deployed on our roads. To this end, active trailer steering (ATS) based on the linear quadratic regulator (LQR) technique has been explored. The LQR-based ATS demonstrates improved maneuverability and enhanced lateral stability. In the ATS design, the vehicle and operating parameters are assumed constant. Thus, it is natural to question the robustness of the ATS in presence of vehicle and operating parameter uncertainties. To address the problem, this paper proposes a robust ATS system. The robust ATS controller is designed using a linear matrix inequality (LMI) based LQR method. In the design, both vehicle and steering actuator parameter uncertainties are considered; to enhance the robustness of the ATS, the weighting matrices of the proposed controller are optimized. The robust controller is applied to an A-Train Double, one type of MTAHV. The effectiveness of the robust ATS is demonstrated using numerical and hardware-in-the-loop real-time simulations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".